A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

Daniel Zennaro, Peter Wellig, Volker M. Koch, George S. Moschytz, Thomas Läubli

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.

Original languageEnglish
Pages (from-to)58-69
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number1
StatePublished - 1 Jan 2003
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received February 25, 2002; revised September 16, 2002. This work was supported by the Swiss National Science Foundation under Project 32-57163.99 and was undertaken as an activity within the project, “Prevention of muscle disorders in operation of computer input devices (PROCID),” a concerted action financed under the European Union research program BIOMED-2 (BMH-98-3903). Asterisk indicates corresponding author. *D.Zennaro is with the Institute of Hygiene and Applied Physiology and the Signal and Information Processing Laboratory, Swiss Federal Institute of Technology Zurich, Zurich 8092, Switzerland (e-mail: daniel.zen-naro@alumni.ethz.ch).


  • Cluster analysis
  • Intramuscular EMG signal decomposition
  • Long-term analyzing
  • Supervised classification
  • Wavelet features


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